Public key algorithms are heavily used in many digital applications including key establishment schemes, secure messaging apps, and digital signature schemes in cryptocurrencies. Recent developments in the field of quantum computation have placed these algorithms at risk as they enable the implementation of more effective attacks to derive the secret key. Most notably Shor’s algorithm exponentially speeds up solving the factoring, discrete logarithm (DLP), and elliptic-curve discrete logarithm (ECDLP) problems. To address this challenge, NIST has initiated a process to develop and standardize a new quantum-resistant public-key cryptographic algorithm. However, asymmetric encryption schemes are known to be computationally intensive, hence energy demanding. The proliferation of energy-constrained internet of things devices, combined with the need to adopt higher complexity quantum resilient cryptographic algorithms, makes it more challenging to continue to use public-key algorithms for all applications. One approach to address these challenges is to adopt symmetric key systems, which are known to be more energy-efficient and more resilient to quantum computers-based attacks. This work performs a comprehensive comparison of energy costs between asymmetric and symmetric key schemes. This comparison is performed using two methods. The first approach uses the energy cost of data usage (ECDU) metric to evaluate the global energy costs associated with internet data usage. It was found that the annual energy consumed by applications associated with public-key cryptography globally is sufficient to provide electricity for 1000 UK households for a year. The second method uses an experimental technique based on constructing a small-scale network of wireless embedded devices. This is subsequently used to compare two key establishment schemes, symmetric and asymmetric, which allows for comparing the computation and communication costs of each solution in a controlled environment, and more importantly estimating the energy consumed by each device participating in the protocol. Our results show that a 58% saving in global energy costs of public key-based applications can be achieved by adopting symmetric key systems. It was also found that a 20% reduction of the energy consumed by a wireless device during a key agreement protocol, can be achieved if symmetric key encryption is used.

Counterfeited products are costing the global economy hundreds of billions of dollars annually. Radio frequency identification(RFID) technology provides a promising solution for this problem, wherein each product is fitted with a secure tag, which is difficult to forge. However, RFID technology is faced with numerous security threats, for example, if the communication link between the reader and the tag is compromised, then it will be possible for a malicious adversary to obtain the private data stored on the device. Tag cloning attacks have also been demonstrated to be feasible, which severely undermines the capabilities of the RFID technology to protect against counterfeiting. One solution to this problem is the use of authentication protocol, however, existing schemes do not support mutual authentication and are still vulnerable to tag cloning attacks. In this paper, a new security mechanism is proposed, which consists of a lightweight three-flights-mutual authentication protocol and an-anti-counterfeit tag design. The proposed solution is based on combining the Rabin encryption scheme with physically unclonable functions (PUF) technology. The protocol security is formally analysed and compares favorably with existing schemes. Our results show that the proposed design is up-to 50% more area-efficient compared to those based on Elliptic Curve Cryptography (ECC).

Concrete is the most widely used material in the building industry due to its affordability, durability, and strength. However, considering carbon emissions, it is believed that concrete will be replaced by geopolymers in the future. As numerous parameters significantly affect the strength of geopolymers, the performance of potential algorithms for strength prediction needs to be evaluated for different binders to select an appropriate algorithm. This study employs machine learning approaches to provide the best prediction method for the flexural strength and compressive strength of geopolymers. A new dataset containing 533 compressive strength and 533 flexural strength values of geopolymers with different binders such as waste glass (GW), obsidian (OB), and fly ash was created. The best prediction solution, with R ² = 0.981 for compressive strength and R ² = 0.898 for flexural strength, was obtained from the extreme gradient boosting (XGBoost) algorithm. Additionally, several other machine learning models were employed, including linear regression, k-nearest neighbors, deep neural network, and random forest, with corresponding determination coefficient (R ²) values of 0.763, 0.804, 0.93, and 0.96, respectively. These models were trained and evaluated using a dataset encompassing features such as binder types, age, and heat, to forecast the mechanical properties of geopolymers. Among these models, XGBoost demonstrated the highest R ² value, indicating superior performance in predicting both compressive and flexural strengths. The findings of this study provide valuable insights into the selection of appropriate machine learning algorithms for predicting mechanical properties in geopolymers, thus contributing to advancements in sustainable construction materials.

Coronavirus disease (Covid-19) caused millions of confirmed cases and thousands of deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early and control the spread of this rapidly expanding pandemic. Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed to forecast of Covid-19 prevalence in Brazil, US, Russia and India. Covid-19 dataset is obtained from World Health Organization website from 22 January, 2020 to 6 January, 2021. Various parameters were tested to select the best ARIMA models for these countries based on the lowest MAPE values (5.21, 11.42, 1.45, 2.72) for Brazil, US, Russia and India, respectively. On the other hand, the proposed BP-ANN model itself provided less satisfactory MAPE values. Finally, the developed new customized hybrid model was achieved to obtain the best MAPE results (4.69, 6.4, 0.63, 2.25) for forecasting of Covid-19 prevalence in Brazil, US, Russia and India, respectively. Those results emphasize the validity of our hybrid model. Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world.

The Internet of Things technology is expected to generate tremendous economic benefits; this promise is undermined by major security threats. This is mainly due to the ubiquitous nature of this technology, which makes it easy for potential adversities to have access to IoT devices and carry well-established attacks. The development of defence mechanisms, in this case, is a challenging task; this is due to the fact that most IoT devices have limited computing and energy resources, which makes it hard to implement classic cryptographic algorithms. This paper addresses this challenge by proposing a lightweight mutual authentication and key agreement protocol named ASSURE based on Rivest Cipher (RC5) and physically unclonable functions (PUFs). To understand the effectiveness of this protocol, a rigorous security analysis under various cyber-attack scenarios is performed. In order to evaluate the overheads of the proposed solution, a wireless sensor network using typical IoT devices called Zolertia Zoul re-mote is constructed. The functionality of the proposed scheme is verified using a server-client configuration. Then, energy consumption and memory utilization are estimated and compared with the existing solutions, namely, the DTLS (datagram transport layer security) handshake protocol in pre-shared secret (key) mode and UDP (user datagram protocol). Experimental analysis results indicate that the proposed protocol can save up to 39.5% energy and uses 14% less memory compared with the DTLS handshake protocol.